Menlo Security Transcends the Almost Secure Cybersecurity Paradigm

Companies of all sizes, across all industries, and from every region of the world all seem to follow the same basic cybersecurity strategy. That would make sense if it worked, but businesses continue to cling to an outdated model of cybersecurity despite overwhelming evidence that it’s not very effective. There is an implicit acceptance that […]

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Big Data in eCommerce: The new trends for 2020

Over the years, Big data technology seems to have transformed several industries across the globe. And it is showing no signs of slowing down. The following post emphasizes how big data makes a significant effect on the present eCommerce trends.

Data is everywhere. In today’s era, we gather and share countless data every moment. And it may interest you to know that each one of our actions is generating data right now. Although, we used to watch several sci-fi movies which now has become a reality. With us being surrounded by so much data, managing them has become very challenging. Big data came into existence! Now you must be wondering what big data is and how it is impacting the eCommerce industry.

Big Data is an alternate method of inspecting a voluminous measure of information to uncover shrouded designs, relationships, advertise patterns, buyer inclinations, and different bits of knowledge that can assist organizations with modifying likewise. Although the concept is nothing new as businesses have been using this process of manually analyzing their data earlier with Big data.

On the other hand, eCommerce is a successful and most-preferred sector in the industry, which has changed the way of buying and selling goods and services. According to Statista, 54% of millennials are now making online purchases compared to 49% of non-millennials. The way to using enormous information to set for your expectations you center around two things in your hover of fitness, the way the world works.

Big Data and Analytics will adjust the substance of eCommerce in 2020

#1 Enhanced Shopping designs

Big Data Analytics is an extraordinary method of understanding the client’s shopping conduct to foresee designs that will improve business techniques. Client’s inclinations, most well-known brands or item that individuals look for, any item that individuals are looking for various occasions yet isn’t offered by you, spikes in requests, what season do clients shop more, et cetera can be evaluated through enormous information investigation

#2 Effective Customer Service 

The Success of any eCommerce lies in how the client feels. Poor services are one of the major turn off for the customer in the long run, maybe forever. Here big data has been extremely helpful in offering e-retailers a golden opportunity to constantly monitor the shopping experience of their end customers so as to provide them better responses for their needs. Right from answering their query to keeping them well-informed about the latest offers and tracking their items. Additionally, huge information can possibly give extraordinary bits of knowledge on client conduct and socioeconomics that will take you the long route in eCommerce improvement domain.

#3 Personalized experience 

It’s been a cutthroat competition in the eCommerce realm and trust me offering personalized experience isn’t something that will set your business apart right away. More than 86% of consumers think that personalization plays an important role in influencing buyers’ decisions. Moreover, big data has the potential to give special insights on customer behavior and demographics that will take you the long way in eCommerce development realm.

With eCommerce big data, you can:-

  • Send emails with customized discounts and special offers to re-engage users.
  • Give personalized shopping recommendations
  • Present targeted ads, as different customers want different yet relevant messaging. Now you must be wondering how this works?
    • First, determine your best customers
    • Create a VIP customer group for those
    • When you launch any new product just ensure that you make those items exclusively available to VIP customers for the time being
    • Offer several discounts that no one else gets

That’s all for now!

eCommerce big data has been extremely helpful when you are planning to survive in this competitive realm. To make things work in the right manner you need to seek for the permission of the user to collect data, create smart programs that offer some value to the end customer, make sure to keep the data small and function within your area of expertise.

From comprehension and breaking down client inclinations and conduct, enormous information investigation makes ready to give better methods of giving upgraded client support. Large Data Analytics even encourages you to comprehend your own organization’s qualities and shortcomings, empowering you to think of better item structures, better-estimating techniques and better serious qualities. To top everything, Big Data Analytics helps the specialists recognize installment cheats using sites and cell phone applications. This encourages endeavors to pipe different installment alternatives using a solitary unified stage, to make installments increasingly helpful.

The post Big Data in eCommerce: The new trends for 2020 appeared first on Big Data Made Simple.

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Jul 02, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Data security  Source

[ AnalyticsWeek BYTES]

>> Why Organizations Are Choosing Talend vs Informatica by analyticsweekpick

>> How Do You Measure Delight? by analyticsweek

>> Using Community Visualizations in Google Data Studio by administrator

Wanna write? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

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Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

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In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Mast… more

[ TIPS & TRICKS OF THE WEEK]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

[ DATA SCIENCE Q&A]

Q:Do we always need the intercept term in a regression model?
A: * It guarantees that the residuals have a zero mean
* It guarantees the least squares slopes estimates are unbiased
* the regression line floats up and down, by adjusting the constant, to a point where the mean of the residuals is zero

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

 @AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

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[ QUOTE OF THE WEEK]

If you can’t explain it simply, you don’t understand it well enough. – Albert Einstein

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

 #BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

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[ FACT OF THE WEEK]

Akamai analyzes 75 million events per day to better target advertisements.

Sourced from: Analytics.CLUB #WEB Newsletter

From Data Scientist to Diplomat

Getting Agreement on “THE PATH TO DEPLOY” your… predictive model, big data application, machine learning based algorithm, segmentation, insights, optimization model…

Battle Scars along the journey to the truth….

When you took your first class in Data Science, R or Linear Regression either at your university or at Coursera did you ever imagine you would struggle with getting your recommendations or analysis implemented?  Did you know you were also joining the diplomatic Corp?

As a good Data Scientist you go through the process of:

  • Defining the problem and objectives for your model or insights project.
  • You gather your information/data for your analysis
  • And you do all of the steps that go into any best practices analytics methods approach. Data Cleansing, Reduction, Validation, Scoring and more. 

When you go to present your analysis or data science results to your clients you end up getting push back in terms of what we call “the path to deploy.”

Frustrating though it may be, hitting this first wall in your data science career, should be viewed, as a learning experience and your greatest teacher, not only in your current role but also for how to assess the organizations analytical maturity (including top management support) when searching for your next role.  Our greatest problems are our greatest teachers.

I have a few painful memories in this area as well, but since then the diplomatic corps has been after me to enroll, one was when I was working with my data science team on behalf of the CMO in a card business to build out a Credit Card Customer Card Acquisition model. The purpose of the predictive model was to make cross selling credit cards to a retail customer easier, more efficient and more profitable.  All Noble and correct goals by all appearances.   The CMO wanted to ensure we acquired customers that not only responded to our offer but use and activate their card.  This strategy made sense not only from a profitability standpoint but also from a customer centric point of view.  The CMO who had agreed with the Decision Science team on the modeling approach and the “path to deploy” was stymied by his boss the CEO who didn’t want to implement the model as the CEO was under pressure to grow the number of card customers and was afraid of not hitting his account/widget goal.  The CMO was very frustrated as was the entire team.  So after much debate we scaled back our full “path to deploy” approach, by offering to set the model up to score and be applied to a smaller netted down number of cross sell leads, so we could prove the value of the model by showing that we may get slightly less customers but they were 5 x more profitable then random.  I call this deferring for collaborative agreement. 

Some Key Learnings

  • So if you think that Data Science is only about science, and trust me we wish it was, then think again. Decision makers are biased by their educations, judgements and what they know about a subject and the more you can show up as helpful and supportive in terms of educating the executives about the statistics and how the modeling works the more supportive they will be.  Think back to when you were hired and someone said that you had “good energy” well what does that mean?  It was a value judgement about how well they liked you, your physical presence during the interview and your ability to articulate your value story.
  • View an initial “No” as a deferral and regroup with the team to come up with an alternative test and learn approach to deploying your analysis or model. Sometimes you need to crawl before you can walk and then run. 
  • Ensure that you ask this question up front of the client: Are you the ultimate decision maker?
  • When you present your analysis results consider presenting a variety of options for executives with more detail around how each option impacts the business. Use data visualization to make your case. 
  • Try and anticipate both positive and negative analysis outcomes upfront and list them as potential challenges in the initial project definition: this way decision makers can decide if they would even go forward with the project. 
  • Use influence maps to determine who can help with pre-socializing your analysis results to convince the decision maker that the outcome is worth testing.
  • Try and understand who are the data science and analytics champions within the company and request that they be your mentors or better yet invite them to key meetings if possible.     

In Conclusion:    Become known as the Data Scientist who can drive collaborative decisions through both horizontal and vertical thinking through:

  • Listening to your clients and or corporate culture is key.
  • Getting agreement on the “path to deploy.”
  • Using Influence management techniques to persuade the client or executives to test the analysis in some way. Don’t consider it rejection just consider it deferring.
  • Demonstrate how the analysis directly aligns to the mission in the organization. For example, many firms are talking about customer centricity and your very analysis is key.  You need to remind people that test and learn and innovation and risk taking is the key to driving toward this vision.    
  • Know and cultivate executive sponsors, evangelists and champions of analytics.  The best firms will have an executive sponsor.

We would love your thoughts and if you find this article helpful please feel free to reach out to our advisors on call or our coaches if you need a sounding board before you react to one of these situations.

Tony Branda

profile-of-a-data-scientistCEO, CustomerIntelligence.net

Originally Posted at: From Data Scientist to Diplomat

Jun 25, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
SQL Database  Source

[ AnalyticsWeek BYTES]

>> September 12, 2016 Health and Biotech Analytics News Roundup by pstein

>> Data Science is more than Machine Learning  by analyticsweek

>> 5 Inbound Marketing Analytics Best Practices by analyticsweekpick

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[ FEATURED COURSE]

The Analytics Edge

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This is an Archived Course
EdX keeps courses open for enrollment after they end to allow learners to explore content and continue learning. All features and materials may not be available, and course content will not be… more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

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A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:Explain selection bias (with regard to a dataset, not variable selection). Why is it important? How can data management procedures such as missing data handling make it worse?
A: * Selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved
Types:
– Sampling bias: systematic error due to a non-random sample of a population causing some members to be less likely to be included than others
– Time interval: a trial may terminated early at an extreme value (ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all the variables have similar means
– Data: “cherry picking”, when specific subsets of the data are chosen to support a conclusion (citing examples of plane crashes as evidence of airline flight being unsafe, while the far more common example of flights that complete safely)
– Studies: performing experiments and reporting only the most favorable results
– Can lead to unaccurate or even erroneous conclusions
– Statistical methods can generally not overcome it

Why data handling make it worse?
– Example: individuals who know or suspect that they are HIV positive are less likely to participate in HIV surveys
– Missing data handling will increase this effect as it’s based on most HIV negative
-Prevalence estimates will be unaccurate

Source

[ VIDEO OF THE WEEK]

Using Analytics to build A #BigData #Workforce

 Using Analytics to build A #BigData #Workforce

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Numbers have an important story to tell. They rely on you to give them a voice. – Stephen Few

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

Subscribe 

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[ FACT OF THE WEEK]

30 Billion pieces of content shared on Facebook every month.

Sourced from: Analytics.CLUB #WEB Newsletter

Eradicating Silos Forever with Linked Enterprise Data

The cry for linked data began innocuously enough with the simple need to share data. It has reverberated among countless verticals, perhaps most ardently in the health care space, encompassing both the public and private sectors. The advantages of linked enterprise data can positively affect any organization’s ROI and include:

  • Greater agility
  • More effective data governance implementation
  • Coherent data integration
  • Decreased time to action for IT
  • Increased trust in data

Still, the greatest impact that linked data has on the enterprise is its penchant to interminably vanquish the silo-based culture that still persists, and which stands squarely in the way of allowing true data culture to manifest.

According to TopQuadrant Managing Director David Price, for many organizations, “The next natural step in cases where they have data about the same thing that comes from different systems is to try to make links between those so they can have one single view about sets of data.”

And, if those links are managed correctly, they may very well lead to the proverbial single version of the truth.

From Linked Open Data…
The concept of linked enterprise data stems directly from linked open data, which has typically operated at the nexus between the public and private sectors (although it can involve either one singularly) and enabled organizations to link to and access data that are not theirs. Because of the uniform approach of semantic technologies, that data is exchangeable with virtually any data management system that utilizes smart data techniques. “As long as we make sure that all of the data that we put in a semantic data lake adheres to standard RDF technology and we use standard ontologies and taxonomies to format the data, they’re already integrated,” said Franz CEO Jans Aasman. “You don’t have to do anything; you can just link them together.” Thus, organizations in the private sector can readily integrate public sector linked open data into their analytics and applications in a time frame that largely bypasses typical pain points of integration and data preparation.

Such celerity could prove influential in massive data sharing endeavors like the Pentagon Papers, in which data were exchanged across international borders and numerous databases to help journalists track down instances of financial fraud. Price is leading TopQuadrant’s involvement in the Virtual Construction for Roads (V-CON) project, in which the company is contributing to an IT system that harmonizes data for road construction between a plethora of public and private sector entities in Holland and Sweden. When asked if TopQuadrant’s input on the project was based on integrating and linking data among these various parties, Price commented, “That’s exactly where the focus is.”

…to Linked Enterprise Data Insight
Linked data technologies engender an identical effect when deployed within the enterprise. In cases in which different departments require the same data for different purposes, or in instances in which there are multiple repositories or applications involving the same data, linked enterprise data can provide a comprehensive data source comprised of numerous tributaries relevant for all applications. “The difference is this stuff is enabled to also allow you to extract all the data and make it available for anybody to download it… and that includes locally,” Price commented. “You get more flexibility and less vendor lock-in by using standards.” In what might be the most compelling use case for linked enterprise data, organizations can also link all of their data–stemming from internal and external sources–for a more profound degree of analytics based on relationship subtleties that semantic technologies instinctively perceive. Cambridge Semantics VP of Marketing John Rueter weighed in on these benefits when leveraged at scale.

“That scale is related to an almost sort of instantaneous querying and results of an entire collection of data. It has eliminated that linear step-wise approach of multiple links or steps to get at that data. The fact that you’re marrying the combination of scale and speed you’re also, I would posit, getting better insights and more precise and accurate results based upon the sets of questions you’re asking given that you’ve got the ability to access and look at all this data.”

Agile Flexibility
Linked enterprise data allows all data systems to share ontologies—semantic models—that readily adjust to include additional models and data types. The degree of flexibility they facilitate is underscored by the decreased amounts of data preparation and maintenance required to sustain what is in effect one linked system. Instead of addressing modeling requirements and system updates individually, linked enterprise data systems handle these facets of data management holistically and, in most instances, singularly. Issuing additional requirements or updating different databases in a linked data system necessitates doing so once in a centralized manner that is simultaneously reflected in the individual components of the linked data systems. “In a semantic technology approach the data model or schema or ontology is actually its own data,” Price revealed. “The schema is just more data and the data in some database that represents me, David Price, can actually be related to different data models at the same time in the same database.” This sort of flexibility makes for a much more agile environment in which IT teams and end users spend less time preparing data, and more reaping their benefits.

Data Governance Ramifications
Although linked enterprise data doesn’t formally affect data governance as defined as the rules, roles, and responsibilities upon which sustainable use of data depends, it greatly improves its implementation. Whether ensuring regulatory compliance or reuse of data, standards-based environments furnish consistent semantics and metadata that are understood in a uniform way—across as many different systems as an enterprise has. One of the most pivotal points for implementing governance policy is ensuring that organizations are utilizing the same terms for the same things, and vice versa. “The difference our technology brings is that things are much more flexible and can be changed more easily, and the relationships between things can be made much more clear,” Price remarked about the impact of linked data on facilitating governance. Furthermore, the uniform approach of linked data standards ensures that “the items that are managed are accurate, complete, have a good definition that’s understandable by discipline experts, and that sometimes have a more general business glossary definition and things like that,” he added.

Security
There are multiple facets of data governance that are tied to security, such as who has the authority to view which data and how. In a linked data environment such security is imperative, particularly when sharing data across the public and private sectors. Quite possibly, security measures are reinforced even more in linked data settings than in others, since they are fortified by conventional security methods and those particular to smart data technologies. The latter involves supplementing traditional data access methods with semantic statements or triples; the former includes any array of conventional methods to protect the enterprise and its data. “The fact that you use a technology that enables things to be public doesn’t mean they have to be,” Price said. “Then you put on your own security policies. It’s all stored in a database that can be secured at various levels of accessing the database.”

Eradicating Silos
Implicit in all of the previously mentioned benefits is the fact that linked enterprise data effectively eradicates the proliferation of silos which has long complicated data management as a whole. Open data standards facilitate much more fluid data integration while decreasing temporal aspects of data preparation, shifting the emphasis on insight and action. This ability to rid the enterprise of silos is one which transcends verticals, a fact which Price readily acknowledged. “Our approach to the V-Con project is that although the organizations involved in this are National Roads Authority, our view is that the problem they are trying to solve is a general one across more than the roads industry.” In fact, it is applicable to the enterprise in general, particularly that which is attempting to sustain its data management in a long-term, streamlined manner to deliver both cost and performance boons.

Source: Eradicating Silos Forever with Linked Enterprise Data

Autonomous vehicles: Three key use cases of advanced analytics shaping the industry

Originally featured on IoT Tech News.

By Akihiro Kurita

Driven by analytics, the culture of the automobile, including conventional wisdom about how it should be owned and driven is changing. Case in point, take the evolution of the autonomous vehicle. Already, the very notion of what a car is capable of is being radically rethought based on specific analytics use cases, and the definition of the ‘connected car’ is evolving daily.

Vehicles can now analyse information from drivers and passengers to provide insights into driving patterns, touch point preferences, digital service usage, and vehicle condition, in virtually real time. This data can be used for a variety of business-driven objectives, including new product development, preventive and predictive maintenance, optimised marketing, up selling, and making data available to third parties. It’s not only powering the vehicle itself, but completely reshaping the industry.

By using a myriad of sensors to inform decisions traditionally made by human operatives, analytics is completely reprogramming the fundamental areas of driving perception, decision making and operational information. In this article, we discuss a few of the key analytics-driven use cases that we are likely to see in the future as this category, (ahem) accelerates.

The revolution of driverless vehicles

Of course, in the autonomous vehicle, the major aspect missing is the driver, traditionally the eyes and ears of the journey. Replicating the human functions is one of the major ways in which analytics is shaping the industry. Based on a series of sensors, the vehicle gathers data on nearby objects, like their size and rate of speed and categorizes them based on how they are likely to behave. Combined with technology that is able to build a 3D map of the road, it helps it then to form a clear picture of its immediate surroundings.

Now the vehicle can see, but it requires analytics to react and progress accordingly taking into account the other means of transportation in the vicinity, for instance. By using data to understand perception, analytics is creating a larger connected network of vehicles that are able to communicate with each other. In making the technology more and more reliable, self-driving vehicles have the potential to eventually become safer than human drivers and replace those in the not so distant future. In fact, a little over one year ago, two self-driving buses were trialed on the public roads of Helsinki, Finland, alongside traffic and commuters. They were the first trials of their kind with the Easymile EZ-10 electric mini-buses, capable of carrying up to 12 people.

Artificial intelligence driving the innovation and decision making

In the autonomous vehicle, one of the major tasks of a machine learning algorithm is continuous rendering of environment and forecasting the changes that are possible to these surroundings. Indeed, the challenge facing autonomous means of transportation is not so much capturing the world around them, but making sense of it. For example, a car can tell when a pedestrian is ready to cross the street by observing behaviour over and over again. Algorithms can sort through what is important, so that the vehicle will not need to push the brakes every time a small bird crosses its path.

That is not to say we are about to become obsolete. For the foreseeable future, human judgement is still critical and we’re not at the stage of abandoning complex judgement calls to algorithms. While we are in the process of ‘handing over’ anything that can be automated with some intelligence, complex human judgement is still needed. As time goes on, Artificial Intelligence (AI) ‘judgement’ will be improved but the balance is delicate – not least because of the clear and obvious concerns over safety.

How can we guarantee road safety?

Staying safe on the road is understandably one of the biggest focuses when it comes to automated means of transportation. A 2017 study by Deloitte found that three-quarters of Americans do not trust autonomous vehicles. Perhaps this is unsurprising as trust in new technology takes time – it took many years before people lost fear of being rocketed through the stratosphere at 500mph in an aeroplane.

There can, and should, be no limit to the analytics being applied to every aspect of autonomous driving – from the manufacturers, to the technology companies, understanding each granular piece of information is critical. But, it is happening. Researchers at the Massachusetts Institute of Technology are asking people worldwide how they think a robot car should handle such life-or-death decisions. Its goal is not just for better algorithms and ethical tenets to guide autonomous vehicles, but to understand what it will take for society to accept the vehicles and use them.

Another big challenge is determining how long fully automated vehicles must be tested before they can be considered safe. They would need to drive hundreds of millions of miles to acquire enough data to demonstrate their safety in terms of deaths or injuries. That’s according to an April 2016 report from think tank RAND Corp. Although, only this month, a mere 18 months since that report was released, professor Amnon Shashua, Mobileye CEO and Intel senior vice president, announced the company has developed a mathematical formula that reportedly ensures that a “self-driving vehicle operates in a responsible manner and does not cause accidents for which it can be blamed”

Transforming transportation and the future

In many industries, such as retail, banking, aviation, and telecoms, companies have long used the data they gather from customers and their connected devices to improve products and services, develop new offerings, and market more effectively. The automotive industry has not had the frequent digital touch points to be able to do the same. The connected vehicle changes all that.

Data is transforming the way we think about transportation and advanced analytics has the potential to make driving more accessible and safe, by creating new insights to open up new opportunities. As advanced analytics and AI become the new paradigm in transportation, the winners will be those who best interpret the information to create responsive vehicles as simple as getting from A to B.

The post Autonomous vehicles: Three key use cases of advanced analytics shaping the industry appeared first on Think Big.

Originally Posted at: Autonomous vehicles: Three key use cases of advanced analytics shaping the industry

Jun 18, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data security  Source

[ AnalyticsWeek BYTES]

>> Multi-Session & Multi-Channel Funnel Reporting in Google Analytics BigQuery by administrator

>> A CS Degree for Data Science — Part I, Efficient Numerical Computation by michael-li

>> Can the internet be decentralized through blockchain technology? by administrator

Wanna write? Click Here

[ FEATURED COURSE]

Artificial Intelligence

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This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances…. more

[ FEATURED READ]

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

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If you are looking for a book to help you understand how the machine learning algorithms “Random Forest” and “Decision Trees” work behind the scenes, then this is a good book for you. Those two algorithms are commonly u… more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:Explain the difference between “long” and “wide” format data. Why would you use one or the other?
A: * Long: one column containing the values and another column listing the context of the value Fam_id year fam_inc

* Wide: each different variable in a separate column
Fam_id fam_inc96 fam_inc97 fam_inc98

Long Vs Wide:
– Data manipulations are much easier when data is in the wide format: summarize, filter
– Program requirements

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Health Informatics Analytics

 @AnalyticsWeek Panel Discussion: Health Informatics Analytics

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[ QUOTE OF THE WEEK]

He uses statistics as a drunken man uses lamp posts—for support rather than for illumination. – Andrew Lang

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

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iTunes  GooglePlay

[ FACT OF THE WEEK]

2.7 Zetabytes of data exist in the digital universe today.

Sourced from: Analytics.CLUB #WEB Newsletter

Choosing an Analytics Development Approach: How to Optimize Your Business Benefits

The question of embedding analytics in an application has moved from “Whether to?” to “How to?” But on the road to embedded analytics, many companies get caught up in the “build versus buy” debate. This inevitably stalls projects and delays time to revenue.

>> Related: Why “Build or Buy?” Is the Wrong Question for Analytics <<

As it turns out, “build or buy” is a false dichotomy. Application teams searching for an analytics solution have a third option: They can take a combined approach by purchasing an analytics development platform and customizing it. This method lets companies get to market faster than building and supports more sophisticated features than buying a bolt-on analytics solution.

How do these three development methods—build, buy, or the combined approach—compare?

The first instinct for many software teams is to build exactly what they want using open-source code libraries and charting components. This may work well, at least until customers began expecting more sophisticated capabilities. According to the 2018 State of Embedded Analytics Report, end users are less satisfied with homegrown analytics compared to a combined approach. In addition, application teams that build analytics on their own using custom code and components see worse results in terms of user experience, differentiation from the competition, and attracting new users.

If building analytics on your own is no longer a realistic option for long-term success, application teams must buy a solution. But they still have two choices: Either embed a bolt-on data discovery tool or take a combined approach and leverage an analytics development platform.

At first glance, buying a solution from a third-party vendor may seem like the way to go—especially if you’ve fallen behind the market and need a way to quickly update your analytics. But time and again, companies that choose a bolt-on approach suffer in the long run. The 2018 State of Embedded Analytics Report shows that teams that buy a bolt-on analytics solution see far fewer business benefits than those choosing to build their own solution or take a combined approach by purchasing an analytics development platform.

Compared to buying a bolt-on solution, a combined approach supports a more differentiated product, improves win rates, reduces customer churn, and boosts overall revenue. It also positively impacts the end users, resulting in better user adoption, user satisfaction, and user experiences.

In particular, a large gap exists when it comes to increasing overall revenue. According to the survey, application teams that took a combined approach when embedding analytics were 19 percentage points more likely to increase revenue than those that bought a bolt-on solution.

This revenue gap may be due in part to an unintended price cap that results from bolt-on solutions. Seventy-four percent of commercial applications taking a combined approach are able to do so. This is significantly more than the 60 percent of commercial companies that bought a bolt-on solution.

Why does the combined approach enable companies to charge more? Because bolt-on solutions tend to support only limited capabilities and focus on commoditized features such as standard interactive dashboards and data visualizations, while taking a combined approach means companies are more likely to embed sophisticated analytics capabilities that set their applications apart from the competition.

It’s clear that companies with the most successful analytic applications have one thing in common: They don’t build OR buy, strictly speaking. Instead, they leverage an analytics development platform to quickly deliver the most robust capabilities to the market.

Read the 2018 State of Embedded Analytics Report >

 

 

Originally Posted at: Choosing an Analytics Development Approach: How to Optimize Your Business Benefits